Various adaptive filter structures have been developed for use in time updated adaptive systems to solve acoustical echo cancellation, channel equalization and other problems; examples of such structures include for example, transversal, multistage lattice, systolic array, and recursive implementations. Among these, transversal finite-impulse-response (FIR) filters are often used, due to stability considerations, and to their versatility and ease of implementation. Many algorithms have also been developed to adapt these filters, including the least-mean-square (LMS), recursive least-squares, sequential regression, and least-squares lattice algorithms.
A seldom used method for adapting the filter coefficients (also called the impulse response) of an adaptive filter is the least squares method. A deficiency of existing methods is that they provide no suitable method for characterizing and using the error function of the adaptive filter when the impulse response is derived using a least squares model.
Consequently, there is a need in the industry for providing filter adaptation unit suitable for producing a set of filter coefficients and characterizing the resulting error function that alleviates at least in part the deficiencies of the prior art.